The development of objective prior distributions for discrete parameter spaces is considered. Formal approaches to such development – such as the reference prior approach – often result in a constant prior for a discrete parameter, which is questionable for problems that exhibit certain types of structure. To take advantage of structure, this article proposes embedding the original problem in a...

Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non-informative priors (e.g., flat or vague priors). One barrier to more informative that must be specified model parameters intercepts, slopes, and sigmas), knowledge often exists the level of response variable. This particularly true for common models like generalized linear mixed have ...

Remote sensing deals with a plethora of sensors, large number classes/categories, and huge variation in geography. Due to the difficulty collecting labeled data uniformly representing all scenarios, data-hungry deep learning models are often trained source domain that is limited above-mentioned aspects. However, during test/inference phase, such subjected distributional shift, also called out-o...